Abstract
Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R2) of 0.70, mean absolute error (MAE) of 639.99 kg ha−1 and root mean squared error (RMSE) of 726.67 kg ha−1.
Highlights
Yield is a quantitative measurement of the crop and it is an important feature that can benefit decision makers by supporting and improving their crop management [1]
Model B presents the highest R2, lowest mean absolute error (MAE) (639.99 kg ha−1 ) and root mean squared error (RMSE) (726.67 kg ha−1 ) followed by model A (MAE—3420.93 kg ha−1 and RMSE—3449.80 kg ha−1 ) and model C (MAE—5267.52 kg ha−1 and RMSE—5391.08 kg ha−1 ). These results indicate that model B presents higher accuracy rate to predict soybean yield based on number of grains from data collected in a single field
In this case, yield prediction based on number of grains (NG) can support on-farm decision making which is the scale of precision agriculture actions that deals with the variability within field levels [88]
Summary
Yield is a quantitative measurement of the crop and it is an important feature that can benefit decision makers by supporting and improving their crop management [1]. Other approaches have been developed to estimate grain yield, but they have a lower spatial resolution, limiting its application on a farm level. Among these approaches, the most common is the application of remote sensing techniques based on agro-meteorological [4] and satellite imagery data [5,6]. The most common is the application of remote sensing techniques based on agro-meteorological [4] and satellite imagery data [5,6] Regarding this kind of data, efforts have been made applying advanced algorithms to estimate yield [7]
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